Wang Research Group

Statistical Genetics · Computational Biology · General Statistical Methodology

We develop statistical methods and machine learning tools for modern biotechnologies and genetic data. Much of our work is motivated by problems in single-cell genomics and complex traits.

Current research directions include:

  • 🧬 Single-cell multi-omics
  • 🎯 Mendelian randomization and causal inference for heritable risk factors
  • 📊 Large-scale inference and multiple testing, including p-value combination methods
  • 🧠 Latent-structure and factor models

News

Jan 17, 2026

Preprint: Improving Estimation Efficiencies for Family-Based GWAS by Integrating Large External Data

A new calibration method to boost efficiency and power in family-based GWAS using external summary statistics.

Dec 6, 2025

Third Place in the Virtual Cell Challenge

Our team achieved 3rd place in the 2025 Virtual Cell Challenge.

Nov 25, 2025

New Commentary on the PDS Metric

We posted a short commentary on Arxiv discussing the behavior and scaling properties of the PDS metric used in the Virtual Cell Challenge and other recent papers.

Aug 7, 2025

Preprint: Validity and Power of Heavy-Tailed Combination Tests under Asymptotic Dependence

New theoretical results on heavy-tailed p-value combination tests under general dependence structures.

Jul 28, 2025

FLOW-MR Published in Nature Communications

Our work on causal mediation analysis for time-varying heritable risk factors using Mendelian Randomization is now published in Nature Communications.

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